'''simple docstring'''




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
  from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						str       ):





         """simple docstring"""



         for model_result in results.values():
             for batch_size, sequence_length in zip(model_result['''bs''']    ,							model_result['''ss''']       ):
                 __lowerCamelCase            =    model_result['''result'''][batch_size][sequence_length]
                 self.assertIsNotNone(a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sgugger/tiny-distilbert-classification'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							only_pretrain_model=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							torchscript=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     @unittest.skipIf(torch_device == '''cpu'''    ,							'''Cant do half precision'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							fpaa=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    AutoConfig.from_pretrained(a       )
         # set architectures equal to `None`
         __lowerCamelCase            =    None
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a    ,							configs=[config]       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )






     @unittest.skipIf(torch_device == '''cpu'''    ,							'''Can\'t do half precision'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							fpaa=a    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    AutoConfig.from_pretrained(a       )
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a    ,							configs=[config]       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tinier_bart'''
         __lowerCamelCase            =    AutoConfig.from_pretrained(a       )
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a    ,							configs=[config]       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         __lowerCamelCase            =    AutoConfig.from_pretrained(a       )
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a    ,							configs=[config]       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tinier_bart'''
         __lowerCamelCase            =    AutoConfig.from_pretrained(a       )
         __lowerCamelCase            =    PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=a    ,							)
         __lowerCamelCase            =    PyTorchBenchmark(a    ,							configs=[config]       )
         __lowerCamelCase            =    benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''
         with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCamelCase            =    PyTorchBenchmarkArguments(
                 models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							save_to_csv=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							inference_time_csv_file=os.path.join(a    ,							'''inf_time.csv'''       )    ,							train_memory_csv_file=os.path.join(a    ,							'''train_mem.csv'''       )    ,							inference_memory_csv_file=os.path.join(a    ,							'''inf_mem.csv'''       )    ,							train_time_csv_file=os.path.join(a    ,							'''train_time.csv'''       )    ,							env_info_csv_file=os.path.join(a    ,							'''env.csv'''       )    ,							multi_process=a    ,							)
             __lowerCamelCase            =    PyTorchBenchmark(a       )
             benchmark.run()
             self.assertTrue(Path(os.path.join(a    ,							'''inf_time.csv'''       )       ).exists()       )
             self.assertTrue(Path(os.path.join(a    ,							'''train_time.csv'''       )       ).exists()       )
             self.assertTrue(Path(os.path.join(a    ,							'''inf_mem.csv'''       )       ).exists()       )
             self.assertTrue(Path(os.path.join(a    ,							'''train_mem.csv'''       )       ).exists()       )
             self.assertTrue(Path(os.path.join(a    ,							'''env.csv'''       )       ).exists()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''sshleifer/tiny-gpt2'''

         def _check_summary_is_not_empty(a						:						Optional[int]       ):
             self.assertTrue(hasattr(a    ,							'''sequential'''       )       )
             self.assertTrue(hasattr(a    ,							'''cumulative'''       )       )
             self.assertTrue(hasattr(a    ,							'''current'''       )       )
             self.assertTrue(hasattr(a    ,							'''total'''       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCamelCase            =    PyTorchBenchmarkArguments(
                 models=[MODEL_ID]    ,							training=a    ,							inference=a    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							log_filename=os.path.join(a    ,							'''log.txt'''       )    ,							log_print=a    ,							trace_memory_line_by_line=a    ,							multi_process=a    ,							)
             __lowerCamelCase            =    PyTorchBenchmark(a       )
             __lowerCamelCase            =    benchmark.run()
             _check_summary_is_not_empty(result.inference_summary       )
             _check_summary_is_not_empty(result.train_summary       )
             self.assertTrue(Path(os.path.join(a    ,							'''log.txt'''       )       ).exists()       )

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations(UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item							) for item in array							)

    return count_of_possible_combinations(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase__							,    UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        __lowerCamelCase            =    sum(
            count_of_possible_combinations_with_dp_array(target - item							,    UpperCamelCase__							)
            for item in array							)
        __lowerCamelCase            =    answer
        return answer

    __lowerCamelCase            =    [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [0] * (target + 1)
    __lowerCamelCase            =    1

    for i in range(1							,    target + 1							):
        for j in range(UpperCamelCase__							):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase					=3
  __UpperCAmelCase					=5
  __UpperCAmelCase					=[1, 2, 5]
  print(combination_sum_iv(n, array, target))
